func.func @main() attributes {tf.entry_function = {inputs = "", outputs = ""}} {
    tf_executor.graph {
      %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
      tf_executor.fetch
    }
    func.return
  }
}

// CHECK-NOT: node

// CHECK: library
// CHECK-NEXT: function

   // OK
   %0 = "tf.Conv2D"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
   // Unsupported data format

      tf_executor.fetch
    }
    func.return
  }
  func.func @foo() {
    tf_executor.graph {
      %0:2 = tf_executor.island {
        %1 = "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<5> : tensor<i32>} : () -> tensor<i32> loc("control_const")
        tf_executor.yield %1 : tensor<i32>
      }
      // CHECK: control_output: "control_const"
      // CHECK:       control_ret {

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.VariableV2"() {dtype = "tfdtype$DT_INT32", value = dense<2> : tensor<i32>, shape = #tf_type.shape<2>, container = "", shared_name = ""} : () -> tensor<!tf_type.int32ref> loc("Ref_Variable")
    %1:2 = tf_executor.island wraps "tf.Identity"(%0#0) : (tensor<!tf_type.int32ref>) -> tensor<*x!tf_type.int32ref> loc("foo")
    tf_executor.fetch
  }
  func.return

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<*xf32>, %arg1: tensor<?x19xf32>) -> tensor<?x19xf32> {
    %0 = "tf.MatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", transpose_a = false, transpose_b = false} : (tensor<*xf32>, tensor<?x19xf32>) -> tensor<?x19xf32>
    func.return %0 : tensor<?x19xf32>
  }
}

// CHECK-LABEL: HloModule main

  %cst2 = arith.constant dense<[1.0, 2.0]> : tensor<2xf32>
  %0 = "tf.Conv2D"(%arg0, %cst0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<1x112x112x3xf32>, tensor<1x3x3x2xf32>) -> tensor<1x28x23x2xf32>

func.func @main() -> (tensor<1x2xf16>, tensor<2xf16>) {
  %graph:2 = tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")
    tf_executor.fetch %0#0, %1#0 : tensor<1x2xf16>, tensor<2xf16>

attributes {tf.entry_function = {inputs = "input0,input1", outputs = "Add"}} {
  %graph = tf_executor.graph {
    %2:2 = tf_executor.island wraps "tf.Add"(%arg0, %arg1) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %2 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {

  %fq = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<f32>, tensor<f32>) -> tensor<3x3x3x16xf32>
  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
  func.return %rst : tensor<256x8x7x16xf32>

  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  func.return %0 : tensor<1x10x10x32xf32>
// CHECK: %[[BIASADD_0:.*]] = "tf.BiasAdd"(%arg0, %arg1) <{data_format = "NHWC"}> {T = "tfdtype$DT_FLOAT"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>